#213

Advanced Computing

CASE

How AI, Edge & Cloud Computing Will Transform the Car
TABLE OF CONTENTS
Introduction

Enabling
CASE

The continued development of connected & autonomous vehicles is enabling more technically advanced userexperiences. As it develops, understanding the technologies influencing the eco-system that enables theseexperiences will benefit OEMs in the short and long-term. The increasing abstraction of hardware and software
enables new thoughts about how to advance automotive computing.

Executive Summary
Technology

Likewise, the presence of these technologies is not limited to one sector, segment, or system. The shift
towards software-defined vehicles will require the evolution of advanced in-vehicle computers, each with adifferent set of technologies and partnerships behind them. Vehicle autonomy already has a similarly expansive
ecosystem, consisting of its own variety of technologies that depend on a growing partnership network. As thislandscape develops, understanding the technologies at the center of it will benefit OEMs in the short and longterm.

Company Profiles
Market Outlook & Recommendations
Go Deeper
Glossary

The Advanced Computing report identifies and analyzes impactful advanced computing technologies being
applied in automotive, applicable use cases, and companies developing them. Further analysis is conducted
into the trends of advanced computing, including an outlook of enabling stages.

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#213/Advanced Computing

Key questions answered> How will advanced
computing enable vehicles
to become more
automated and immersive?

> What technologies andsystems will provide thehighest impact to OEMs andsuppliers?

> Who are the leading
players deploying
advanced computing inAutomotive?

> How can OEMs work withtech partners while
maintaining control over theoverall stack?

This research supports

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June 2022

Advanced Computing

How AI, edge & cloud computing will transform the car

Contents
Advanced Computing
Introduction≫ ≫Introduction

4

Executive Summary ≫5Market Drivers ≫11

Executive Summary ≫•Introduction

Section Name ≫X
X

1: ADAS Functional Expectations▪ Subsection• 2: ADAS HMI Richness• ▪3: Subsection
Immersive HMI•

XCompanyProfiles
SectionName≫ ≫▪ Introduction▪ Subsection▪ Understanding The Profiles▪ Subsection▪ OEMs▪ Subsection• BMW41X• Mercedes-Benz• Volkswagen▪ Subsection▪ Tier 1 Hardware Suppliers• Continental•

Outlook - Toward Dynamic WorkloadManagement•

Trends•Recommendations

Go Deeper ≫82

Glossary ≫85

Contact Us ≫8653• Aptiv• BoschContactUs ≫Introduction

69X▪ Subsection• Tesla• ▪4: Subsection
ECU Consolidation• 5: OTA SW Updates Needed for New Features▪ Subsection• 6: OTA SW Updates for Fixes
Subsection• ▪Additional
Market Drivers•
45• GMSection
Name ≫• NIO

Market Outlook & Recommendations ≫X• DensoSection Name ≫Technology ≫▪ Subsection▪ Introduction▪ ▪1: Subsection
High-Performance Computer Platforms

20▪ ▪2: Subsection
Virtualization - In-Car Containers▪ ▪3: Subsection
Network Edge Computing▪ 4: MLOps - Machine Learning Pipeline for Data
▪ 5: SoC UpgradesX• Huawei▪ Cloud Suppliers59• Alphabet• Amazon Web Services• Baidu• Microsoft• Red Hat

▪ SoC Suppliers▪ 6: Quantum Technology & Cryptographic Effects• Intel▪ Additional New Technologies• Nvidia• Qualcomm

65Introduction

Introduction

Introducing the Advanced Computing in Automotive Report
Computing moved from the mainframe to the home personal computer and then to thefamily car long ago. Improvements from other information technology areas have madetheir way into automotive through long periods of identifying an expected in-vehicle
benefit and creating automotive-grade hardened hardware capable of withstanding theharsh automotive environment.
The pace of adoption has increased as a larger portion of vehicle features become
software-defined. As in-vehicle hardware reaches even higher levels, it is now reasonable
to discuss providing in-vehicle software across multiple systems independently of theunderlying hardware. This idea of abstraction of hardware and software enables new
thoughts about how to advance automotive computing.
Cloud computing powerhouses and embedded automotive system-on-chip designers areproviding advancements in how automotive computing is conceived. However, OEMs
judiciously are researching their own desired path to optimum features as they seek togrow their reputation as providers of automotive tech expertise. The features taken tomarket, and the hardware advancements supporting them, must be focused on providing
transportation value and differentiating experiences to the automotive consumer. Tooptimize the use of technology, each OEM and its strategic suppliers should addressquestions about their view of advanced computing.

What are the key questions for Advanced Computing for Automotive?
•

What expected features are driving technology changes?

•

What technologies and systems will provide the highest impact to OEMs and suppliers?​•

How can OEMs design tech architectures to increase control of technology instead ofbeing controlled by it?

6Section

Executive SummaryMarket Drivers

Content
Introduction to the topics, technologies, and outlook considered inthis report, and resulting recommendations to benefit from them.
Conclusion: Preparation will provide flexibility for future
performance and cost decisions for automotive computing.
Discussion of consumer-facing features and business benefiting
initiatives that are pushing software-defined vehicles and newcomputing architectures into reality.
Conclusion: To meet customer expectations, the industry isseeking both computation speed and new development options.

Introduction to technologies and advancements set to change themeaning of computing for automotive.
TechnologyCompany Profiles

Outlook andRecommendations

Conclusion: Technologies developed with faster performance arealso bringing the ability to expand development past vehicledelivery dates
Overview of automotive companies active in advanced computing
including OEMs, Tier 1s, SoC suppliers, and Cloud services, interms of activities, investments, and technology insights.
Conclusion: Trends include function integration, architectural
simplification, and hardware abstraction, with autonomous driving
functions being the showcase applications.
Discussion of the impacts of technologies, decisions that will be
made, and recommendations to maximize results of futurecomputing.

Conclusion: Technology adoption and process changes together
will allow optimization of near-term and long-term computing
infrastructure.

Go Deeper

Can SBD help you with any unanswered questions?

Glossary

Expansion of abbreviations and acronyms
6

Example slides
from the reportMarket Drivers

Key Market Driver

Key Market Driver 2: Rich HMI for ADAS visualizations
Graphically rich and well-designed HMI improves ADAS
Collaborative driving needs easy situational awareness, which is provided by clear
detailed UX visualizing the multitude of sensors and lane-specific road data

Safety••••Vehicle systems affected by featuresFeatureRich HMI forADASvisualizations

Headunitdisplay

InstrumentCluster
display

✔

✔

Cabin

ConnectivitySensors

Powertrain✔

✔

Relevant information
Easy to understandFeedback of control adjustments
Clear warning signals through visual signals and colors

Appeal••••Match the style of the brandHighlights availability of features
Uncluttered clean design
Customizability

Convenience
•
•
•
•
•

Communication of vehicle actions
Functional and intuitive interface
Communication what sensed by the vehicle
Communication of limitations
Ease of use*ADAS photos taken from SBD 635 HMI Benchmarking testing for (first row) Rivian R1T,NIO ES8, (second row, 2nd photo) Mercedes-Benz S-Class, (third row) BMW iX, Lucid Air.
Second row, 1st photo, Tesla Model 3 source: Whole Mars Catalog

88

HPC Platforms

Technology

HPCs – Hardware & Software ArchitectureHigh-performance computers are the key to abstraction
High-Performance Computer (HPC)
Applications
Real-TimeOperating
System (RTOS)

Containers

General Purpose
Operating System (GPOS)
Type 1 Hypervisor

Non-Volatile
MemoryDSPVolatile
MemoryHW
Accelerator

EthernetCAN
FlexRay

CPU Cluster for
CPU Cluster for
RTOS
RTOS

CPU Cluster for
CPU Cluster for
RTOS
GPOS

Graphics
Processing
Unit(AV & IVI)USBPCIe• Applications & containers leverage services on the GPOS torun portable software integrated with the hardware
abstraction services offered by the GPOS or othermiddleware.
• The RTOS provides deterministic computing for safetycritical functions, while the GPOS allows the deployment of
more general services, features, and data processing.
• Type 1 hypervisor provides hardware-optimized
virtualization services, ensuring safe operation of the RTOS
alongside GPOS guest OS(es).

• HPCs provide a variety of physical interfaces to vehicle subnetworks and allow high-bandwidth communication withother components via Ethernet, USB, and PCIe.
• GPUs power the processing of camera & radar data for
ADAS/AV applications as well as power rendering for digital
cockpit interfaces.
• CPU clusters ensure redundant processing for both RTOS
and GPOS applications.
• DSPs and hardware accelerators provide efficient low
latency FIR & IIR filter processing for infotainment audio
and noise cancellation features.

• Multiple HPCs may be deployed to provide high availability,optimized, redundant AV services, and/or specialized digital
cockpit applications.
99

Company Profiles

Understanding the profiles

Understanding The Profiles – Radar Chart Scoring
Definition

Common Scoring SystemStageLevelNoActivity

0Not part of offering or strategy

Research1

Planned or announced as part of future strategy

Testing2

Proof of concept / prototype demonstrated• HPC Hardware – Body, Motion Control, ADAS

Launch3In-market offering available, or expected within 6 months• AI, Machine Learning, Data Management

Refine4Competitive offering

Lead

5

Market leadership and continued advancement

These are the standard scoring parameters used in the radar chart present ineach company profile slide.
These rating criteria have been used for the majority of factors:• HPC Hardware – Cockpit, Gateway, Connectivity• Compute Stack• Virtualization In-car• Cloud Service

Alternative Scoring
Quantum Computing – A different ranking has been used to take into
consideration the complexity and current status of the development of thistechnology.
StageNoActivity

ResearchLevel

Definition

Edge/Near-car Computing – The different scoring reflects C-V2X deploymentand involvement in a consortium as indicators of high skill in this space, morethan commercial competition with others, as observed in the common ranking.
Stage

Level

Definition0

No activity

No Activity0

Not part of offering or strategy

1

Unproven concept, no public testing has been performed

Research

1

Announced targets or research projects

2

Research announcement

2

Small scale prototype tested in intended environment

3

Prototype system tested in intended environment close toexpected performance

Testing

3

Active investigations with others in either quantum computing
or quantum resistance for cryptography

Testing

4

Planned quantum as a service

Launch4

Edge computing or C-V2X launched

10Launch5

Offer quantum to others as cloud services

Lead5Involvement with consortiums such as AECC
10Profiles ⅼ SoC Suppliers ⅼNvidia

SoC Supplier - Nvidia
Overview
NVIDIA is known primarily for its manufacturing of high-end graphics processing units (GPUs) supporting IVI and SoCsfor autonomous vehicles (AV). The latest development aim at covering everything from the car to the data center. WithHyperion, Nvidia provides a complete modular platform for AV development.

Autonomous Vehicles Ecosystem

Partnerships

Nvidia DRIVE powerful computing platforms create anecosystem for AVs ranging from hardware to software.

2015 - Drive PXintroduction. First
H/W optimizedfor AD.

DRIVE Hyperion is an AV platform that integrates
Nvidia's SoC Orin-based AI compute with a sensor
suite of 12 exterior cameras, three interior cameras,nine radars, 12 ultrasonics, and one front-facing lidar,
plus one lidar for ground truth data collection. Also,
Hyperion features the full software stack forautonomous driving as well as driver monitoring andvisualization, which is ready for OTA updates.
Nvidia also offers a broad variety of SoCs and GPUs
through the developer kits DRIVE Orin, AGX Pegasus,
and AGX Xavier, and looks ahead already withupcoming ones like Atlan. The SoCs offered also as
integrations of its Hyperion platform are designed toenable level 2 to level 5 autonomy, as well as
robotaxis. Their AI-focused processors like H100
Hopper allow the training of advanced AI models.
Strengths• Nvidia enjoys already a strong presence inautomotive with numerous partnerships with thelargest OEMs in the world in different market
segments.

11Quantum
Computing andresilience

HPC h/w - cockpit,
gatewayconnectivity
543Acquisitions &Investments

HPC h/w - body,
motion control,ADAS•

2022 – Acquisition of Excelero. The high-performanceblock storage provider will have its technology
integrated into Nvidia’s enterprise software stack.

•2022 – Acquisition of Bright Computing, a leader in
HPC cluster management solutions. Amsterdam-based
Bright Computing spun out of HPC firm ClusterVision
and automates administration for clusters.

•2021 – Acquisition of Oski Technology, a company
developing
comprehensive
formalverification
methodologies to identify and sign-off high-risk
blocks, achieve system-level architecture sign-off, andquickly resolve post-silicon bugs.

•

2021 – Acquisition of DeepMap, a start-up dedicatedto building high-definition maps for autonomousvehicles. The HD mapping solution combines digital
camera images with lidar to build 3D maps that
represent real-time road data.

2
1
Cloud Services

0Edge/Near car
computing
SoC Sup.
Avg.

AI, Machinelearning, data
management

Compute stack
Virtualization in-car

2019 –Introduction ofDRIVE AGX Orin
— Advanced,
Software-Defined
Platform for AD

2025 – Nvidia
Drive’s new
Compute Atlan
enters production11

Outlook

Market Outlook & Recommendations

Obstacles to Dynamic Workload ManagementFuture State

Disruptive OEMs & Hardware Suppliers• OEMs will need to reorient processes andcost performance metrics away from
Binary parity
focusing narrowly on single-vehicle
containersmodels or single fiscal years. Cost
engineering and purchasing groups willneed to see the long-term gains possible
with an initial investment• HPC designers need to focus on
generalized hardware, allowing use as
cross-domain H/W to offer in-vehicle
redundancy for critical workloads Car-cloudand
parallel computing speed increases.
Ifhardware
designers specialize in custom hardware
abstractionto specific input/outputs and workloads,flexibility will be limited.
• Network Edge computing is initiallylimited by narrow use-case deployments
such as 5G positioning services and low
latency applications.

In-carDynamic
Incremental Improvement‘H/W runs
optimal S/W forthe moment’• OEMs and Tier 1s need to develop systems
with a fast follower pace, to allow gaining
and keeping compatibility with marketleading software management APIs anddeployment architectures.

Disruptive Software Suppliers• Extended investment in multiple technology
paths may be needed while developing
enough experience to determine the best fit
for company requirements and systemarchitectures.
• Software development groups assisted bycybersecurity expertise need to implement
Today quantum-resistant cryptographic algorithms
and key storage through planned systemdesigns to avoid rushed patches after
exploited vulnerabilities become public.

Fixed• As‘H/W runs
S/Wautomotive SoCs gain significance invehicle
design, OEMs need to decide to buy,
designed forit’build, or partner to make available
customized differentiating SoC features.

12DefinedSoftwareAllocation

Configurable
WorkloadManagement• System architects need to discover methods
to interface with legacy protocols duringtransition periods. Compatibility withhardware and software should be done sothat new beneficial changes can quickly
proceed without abandoning commitments
to existing products.
•Containers’ greater benefit will needorganization and cross-organization
standardization leading to reuse andhardware portability.

• Machine Learning (ML) needs acceptance asfunctionally safe through the developmentof explainable ML or a determination of a
sufficient amount of testing.

AutomatedWorkloadManagement12

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